英文文献：Discrete choice models, which one performs better?-离散选择模型，哪一个表现更好?在过去的三十年里，多项式logit模型一直是选择模型的标准
英文文献作者：Gallardo, Rosa Karina,Chang, Jae Bong
英文文献摘要：
For over the last thirty years the multinomial logit model has been the standard in choice modeling. Development in econometrics and computational algorithms has led to the increasing tendency to opt for more flexible models able to depict more realistically choice behavior. This study compares three discrete choice models, the standard multinomial logit, the error components logit, and the random parameters logit. Data were obtained from two choice experiments conducted to investigate consumers’ preferences for fresh pears receiving several postharvest treatments. Model comparisons consisted of in-sample and holdout sample evaluations. Results show that product characteristics hence, datasets, influence model performance. We also found that the multinomial logit model outperformed in at least one of three evaluations in both datasets. Overall, findings signal the need for further studies controlling for context and dataset to have more conclusive cues for discrete choice models capabilities.

计量经济学和计算算法的发展导致了越来越多的倾向选择更灵活的模型，能够描绘更现实的选择行为。本文比较了三种离散选择模型:标准多项式模型、误差分量模型和随机参数模型。数据来自两个选择实验，调查消费者对梨采后处理的偏好。模型比较包括样本内评价和保留样本评价。结果表明，产品特征因此，数据集，影响模型性能。我们还发现多项式logit模型在两个数据集的三个评估中至少有一个表现更好。总的来说，研究结果表明需要进一步的研究来控制上下文和数据集，为离散选择模型的能力提供更多的结论性线索。